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学者姓名：钟香玉
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Abstract ：
In large group decision-making (LGDM), some decision-makers (DMs) may engage in manipulative behaviors driven by personal interests, while others may become susceptible to manipulation due to the complexity and uncertainty of the decision-making process. These manipulative and manipulated behaviors hinder the effective achievement of group consensus and undermine the fairness and acceptability of the decision-making process. To address this, we propose a two-stage consensus model that accounts for both manipulative and manipulated behaviors. First, the trust relationships among DMs are adjusted based on the similarity of their evaluations, and the strength of these relationships is calculated using their adjusted mutual trust degrees. Next, a clustering method based on the fracture of relationship strength is introduced to classify DMs into subgroups. By considering DMs' hesitancy, trust relationships, and preference degrees for various alternatives expressed in their evaluations, manipulators are identified and penalized with a weight penalty. The combination of hesitation degree, trust degree, and similarities in alternative ordinals, before and after subjective adjustment, is used to identify and impose penalties on manipulated DMs. Furthermore, various objective adjustment strategies are proposed to better manage the different behaviors of DMs, thereby improving decision-making efficiency and consensus. Finally, an application example and comparative analyses are presented to validate the feasibility of the proposed method. The proposed method effectively manages manipulative and manipulated behaviors, significantly enhancing consensus efficiency, fairness, and acceptability in the decision-making process. © 2025 Elsevier B.V.
Keyword ：
Consumer behavior Consumer behavior Decision making Decision making Emotional intelligence Emotional intelligence Social behavior Social behavior
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| GB/T 7714 | Zhong, Xiangyu , Liu, Fuhao , Du, Zhijiao et al. A two-stage consensus model incorporating manipulative and manipulated behaviors for large group decision-making under social network environment [J]. | Applied Soft Computing , 2025 , 178 . |
| MLA | Zhong, Xiangyu et al. "A two-stage consensus model incorporating manipulative and manipulated behaviors for large group decision-making under social network environment" . | Applied Soft Computing 178 (2025) . |
| APA | Zhong, Xiangyu , Liu, Fuhao , Du, Zhijiao , Wan, Qifeng . A two-stage consensus model incorporating manipulative and manipulated behaviors for large group decision-making under social network environment . | Applied Soft Computing , 2025 , 178 . |
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Abstract ：
As an extension of group decision-making in terms of scale and uncertainty, linguistic Z-number large-scale decision-making (LZ-LSDM) is emerging as a prominent research topic in the field of decision science. The unique structure of LZ-LSDM presents new challenges for both clustering analysis and consensus building. Minimum-cost consensus (MCC) based on the optimization principle is widely recognized as an effective tool for managing the consensus-reaching process. However, there is a scarcity of literature that addresses the study of MCC within the context of LZ-LSDM, as well as the application of MCC in the identification and treatment of noncooperative behaviors. To this end, this study proposes a punishment strategy-driven multi-stage type-alpha constrained MCC model for LZ-LSDM problems. First, a similarity constraint-based clustering method with linguistic Z-numbers is proposed. Given the clustering results, a type-alpha constrained MCC (alpha-CMCC) model with personalized feedback constraints is designed to provide a personalized solution for visualizing opinion adjustment and preventing over-adjustment. Based on the optimal solution obtained by alpha-CMCC, the identification rule for noncooperative behaviors is proposed. We conclude three punishment strategies-namely, pure, mixed, and cross-to address non-cooperative behaviors by arranging and combining commonly used punishment approaches. Finally, we illustrate the feasibility and validity of the model through a case study designed to facilitate consensus among an online patient community on knowledge-based recommendations. An exhaustive comparative analysis reveals the advantages and features of the proposed consensus model.
Keyword ：
Group knowledge recommendation consensus Group knowledge recommendation consensus Linguistic Z-number large-scale decision-making Linguistic Z-number large-scale decision-making Multi-stage type-alpha constrained minimum-cost consensus Multi-stage type-alpha constrained minimum-cost consensus Non-cooperative behavior Non-cooperative behavior Personalized feedback constraint Personalized feedback constraint Punishment strategy Punishment strategy
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| GB/T 7714 | Du, Zhijiao , Yu, Sumin , Guo, Leilei et al. Multi-stage type-α constrained minimum-cost consensus for linguistic Z-number large-scale decision-making [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 136 . |
| MLA | Du, Zhijiao et al. "Multi-stage type-α constrained minimum-cost consensus for linguistic Z-number large-scale decision-making" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 136 (2024) . |
| APA | Du, Zhijiao , Yu, Sumin , Guo, Leilei , Zhong, Xiangyu . Multi-stage type-α constrained minimum-cost consensus for linguistic Z-number large-scale decision-making . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 136 . |
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Abstract ：
In the context of large group decision-making (LGDM), the opinions of individuals can influence each other due to their trust relationships. So, trust relationships should be deemed as just as important as evaluation information, and they should be considered jointly throughout the LGDM. This study first transforms the trust relationships between decision-makers into an information type, labeled as compromise information, whose form is the same as the evaluation information. The compromise information is utilized to incorporate trust relationships into various stages of the decision-making process, including clustering, weight determination, consensus reaching, and alternative selection. In the expert clustering and weight determination processes, more criteria and factors are considered by considering the compromise information. In the consensus reaching process, an optimization model is built to adjust the evaluation information of clusters to simultaneously guarantee a substantial increase in the global consensus level and minimize the adjustment cost. The compromise information also serves as a reference to limit the range of the adjusted information. An objective method to determine the consensus threshold is proposed. The proposed method is validated through an application example and comparisons, demonstrating its rationality and effectiveness. Simulation results indicate that the proposed consensus reaching method converges regardless of the number of experts, alternatives, and criteria. The proposed method integrates evaluation information and trust relationships into the LGDM process, thereby improving the rationality and scientificity of the decision results.
Keyword ：
Cluster weights Cluster weights Consensus reaching process Consensus reaching process Expert clustering Expert clustering Large group decision-making Large group decision-making Social network analysis Social network analysis
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| GB/T 7714 | Zhong, Xiangyu , Xu, Xuanhua , Goh, Mark et al. Large Group Decision-Making Method Based on Social Network Analysis: Integrating Evaluation Information and Trust Relationships [J]. | COGNITIVE COMPUTATION , 2023 , 16 (1) : 86-106 . |
| MLA | Zhong, Xiangyu et al. "Large Group Decision-Making Method Based on Social Network Analysis: Integrating Evaluation Information and Trust Relationships" . | COGNITIVE COMPUTATION 16 . 1 (2023) : 86-106 . |
| APA | Zhong, Xiangyu , Xu, Xuanhua , Goh, Mark , Pan, Bin . Large Group Decision-Making Method Based on Social Network Analysis: Integrating Evaluation Information and Trust Relationships . | COGNITIVE COMPUTATION , 2023 , 16 (1) , 86-106 . |
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